Systems and methods for analyzing network data to identify human and non-human users in network communications

ABSTRACT

Systems and methods are disclosed for identifying human users on a network. One method includes receiving network data comprising data transmitted over a network over predetermined time period, the network data comprising a plurality of usernames and a plurality of events, wherein each of the plurality of events is associated with at least one of the plurality of usernames; determining a plurality of pairs, each pair of the plurality of pairs comprising a username of the plurality of usernames and an associated event of the plurality of events; determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more predetermined event frequency thresholds; determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more predetermined event frequency thresholds; generating at least one distribution associated with the qualifying pairs and non-qualifying pairs; and based on the at least one distribution, determining if at least one username of the plurality of usernames is associated with a human user or a non-human user.

TECHNICAL FIELD

The present disclosure relates to systems and methods for identifyinghuman users in electronic networks.

BACKGROUND

In addition to typical Internet traffic coming from Internet users, manywebsites are subjected to various forms of malicious traffic. Malicioususers and bots may flood websites with comment spam, links to malicioussoftware, and ingenuine clicks, visits, hits, etc. Users of electronicmessaging systems, such as electronic mail, texting, and social mediaapplications, may also disseminate spam and other ingenuine links andmaterials. Various forms of fraud and fraudulent solicitations may alsobe disseminated.

Conducting effective anti-abuse often depends upon being able toaccurately distinguish abusive/fraudulent users from genuine users. Yet,abusers commonly alter their behavior to better mimic genuine users,creating an arms race between abuse detection techniques and detectionavoidance techniques.

Accordingly, solutions are needed to be able to more accurately identifyhuman from non-human accounts.

SUMMARY OF THE DISCLOSURE

Embodiments of the present disclosure include systems and methods foridentifying human users on a network.

According to certain embodiments, computer-implemented methods aredisclosed for identifying human users on a network. One method includesreceiving network data comprising data transmitted over a network overpredetermined time period, the network data comprising a plurality ofusernames and a plurality of events, wherein each of the plurality ofevents is associated with at least one of the plurality of usernames;determining a plurality of pairs, each pair of the plurality of pairscomprising a username of the plurality of usernames and an associatedevent of the plurality of events; determining qualifying pairs of theplurality of pairs, the qualifying pairs corresponding to a subset ofthe plurality of pairs that meet or exceed one or more predeterminedevent frequency thresholds; determining non-qualifying pairs of theplurality of pairs, the non-qualifying pairs corresponding to the subsetof the plurality of pairs that do not meet or exceed one or morepredetermined event frequency thresholds; generating at least onedistribution associated with the qualifying pairs and non-qualifyingpairs; and based on the at least one distribution, determining if atleast one username of the plurality of usernames is associated with ahuman user or a non-human user.

According to certain embodiments, systems are disclosed for identifyinghuman users on a network. One system includes a data storage device thatstores instructions for identifying human users on a network; and aprocessor configured to execute the instructions to perform a methodincluding: receiving network data comprising data transmitted over anetwork over predetermined time period, the network data comprising aplurality of usernames and a plurality of events, wherein each of theplurality of events is associated with at least one of the plurality ofusernames; determining a plurality of pairs, each pair of the pluralityof pairs comprising a username of the plurality of usernames and anassociated event of the plurality of events; determining qualifyingpairs of the plurality of pairs, the qualifying pairs corresponding to asubset of the plurality of pairs that meet or exceed one or morepredetermined event frequency thresholds; determining non-qualifyingpairs of the plurality of pairs, the non-qualifying pairs correspondingto the subset of the plurality of pairs that do not meet or exceed oneor more predetermined event frequency thresholds; generating at leastone distribution associated with the qualifying pairs and non-qualifyingpairs; and based on the at least one distribution, determining if atleast one username of the plurality of usernames is associated with ahuman user or a non-human user.

According to certain embodiments, a non-transitory computer readablemedium is disclosed that stores instructions that, when executed by acomputer, cause the computer to perform a method for identifying humanusers on a network. One method includes receiving network datacomprising data transmitted over a network over predetermined timeperiod, the network data comprising a plurality of usernames and aplurality of events, wherein each of the plurality of events isassociated with at least one of the plurality of usernames; determininga plurality of pairs, each pair of the plurality of pairs comprising ausername of the plurality of usernames and an associated event of theplurality of events; determining qualifying pairs of the plurality ofpairs, the qualifying pairs corresponding to a subset of the pluralityof pairs that meet or exceed one or more predetermined event frequencythresholds; determining non-qualifying pairs of the plurality of pairs,the non-qualifying pairs corresponding to the subset of the plurality ofpairs that do not meet or exceed one or more predetermined eventfrequency thresholds; generating at least one distribution associatedwith the qualifying pairs and non-qualifying pairs; and based on the atleast one distribution, determining if at least one username of theplurality of usernames is associated with a human user or a non-humanuser.

Additional objects and advantages of the disclosed embodiments will beset forth in part in the description that follows, and in part will beapparent from the description, or may be learned by practice of thedisclosed embodiments. The objects and advantages of the disclosedembodiments will be realized and attained by means of the elements andcombinations particularly pointed out in the appended claims.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the scope of disclosed embodiments, as setforth by the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various exemplary embodiments andtogether with the description, serve to explain the principles of thedisclosed embodiments.

FIG. 1 depicts a schematic diagram of a network environment in whichmethods are practiced for identifying human users on a network,according to an exemplary embodiment of the present disclosure;

FIG. 2 depicts a flow diagram of exemplary methods for identifying humanusers on a network, according to an exemplary embodiment of the presentdisclosure;

FIGS. 3A-3D depict graphs associated with exemplary methods foridentifying human users on a network, according to an exemplaryembodiment of the present disclosure;

FIG. 4 depicts a flow diagram of an exemplary method for identifyinghuman users on a network, according to an exemplary embodiment of thepresent disclosure; and

FIG. 5 is a simplified functional block diagram of a computer that maybe configured as a device for executing the methods of FIGS. 2-4,according to exemplary embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Specific embodiments of the present disclosure will now be describedwith reference to the drawings. As will be recognized, the presentdisclosure is not limited to these particular embodiments. For instance,although particular steps in particular embodiments may be discussed,steps from one embodiment may be used in other embodiments. Further, anystep discussed in relation to any particular embodiment may be optional.This may be because, for example, certain steps may enhance theeffectiveness of certain embodiments, while not strictly being necessaryfor the embodiment to function as intended.

The present disclosure relates to systems and methods for identifyinghuman users on a network. Many websites experience various forms ofmalicious or fraudulent traffic. Sites may receive comment and postingspam, or other malicious or spam content submissions. Malicious usersand/or bots may further drive up hits, visitors, or ad views/clicks forvarious purposes. For example, malicious bots may interact withadvertisements to generate additional ad revenues for website owners orad networks. Non-human users engaging in spam dissemination or maliciousactivity is particularly prevalent in electronic messaging applicationssuch as e-mail, text, and social messaging applications.

The ability to quickly and efficiently identify features in data whichcan help separate human users from non-human users is of greatimportance. However, some possible techniques for detecting non-humanusers are time-consuming and unreliable. Generating data sets andrunning queries may be slow, and validating any output may be difficult.Additionally, some possible techniques may have a low success rate infinding features that reliably distinguish between human and non-humanaccounts.

FIG. 1 depicts a schematic diagram of an exemplary network environment100 for identifying human users on a network, according to an exemplaryembodiment of the present disclosure. As shown in FIG. 1, theenvironment 100 may include at least one network device 110 a-n, anelectronic network 115, at least one publisher server 120 a-n, and atleast one traffic analysis server 125. Network devices 110 a-n mayinclude laptop and desktop computers, Internet-enabled mobile devices,or any Internet-enabled device. Electronic network 115 may be, forexample, the Internet, but may also be or comprise a Local Area Network(LAN), Wide Area Network (WAN), Wireless Local Area Network (WLAN),Metropolitan Area Network (MAN), and/or Storage Area Network (SAN), etc.A website may be hosted by a publisher server 120 so that it is madeaccessible to one or more of network devices 110 a-n. The publisherserver 120, which may be a content delivery network (CDN), may furtherdetermine an ad server and/or an ad exchange to provide an ad includedin the website provided to a network device 110. The traffic analysisserver 125 may analyze network traffic exchanged between network devices110 a-n and publisher servers 120 a-n in accordance with techniquespresented herein. Although the traffic analysis server 125 is depictedas separate from the publisher servers 120 a-n, in some embodiments, thefunctions performed by the traffic analysis server 125 may be performedon a publisher server 120. Further, steps of the methods depicted inFIGS. 2-4 may be practiced on a combination of devices depicted in FIG.1.

FIG. 2 depicts a flow diagram of an exemplary method 200 for identifyinghuman users on a network, according to an exemplary embodiment of thepresent disclosure. Although the steps shown in FIGS. 2 and 4 arenumbered sequentially for purposes of explanation, as will be apparent,the steps do not necessarily need to be performed in the listed order.Further, while FIGS. 2 and 4 are discussed separately, steps discussedin relation to, for example, FIG. 2 may be implemented in the context ofFIG. 4, and vice versa. Generally speaking, techniques practiced inrelation to any one figure may be practiced in relation to any otherfigure. Techniques discussed in relation to FIGS. 2-4 may be performedon, for example, one or more traffic analysis servers 125, one or morepublisher servers 120 a-n, a combination of the two, or in combinationwith other network-accessible devices.

As shown in FIG. 2, a system, such as traffic analysis server 125, atstep 205 may receive data, such as web traffic data, over electronicnetwork 115. The data may be raw data, or processed and/or formatteddata. The data may be forwarded by one or more publisher servers 120a-n, or received directly as the traffic analysis server 125 acts as aproxy. For example, any or all data sent between network devices 110 a-nand publisher server 120 may first pass through traffic analysis server125. Alternatively, the data may be periodically forwarded to thetraffic analysis server 125 in batch form, or as it is sent or receivedto and/or from the publisher server 120. The data received may comprisebrowsing and other website interaction data, and/or electronic messagingdata. The basic data types received may be referred to as variables,such as sender and/or destination Internet Protocol (IP) addresses,dates associated with the traffic, usernames or other user identifiers,sender information and/or sender identifiers, read or ignored data,success/failure to authenticate information, dated/timestampeduser-action/event pairs, whether electronic messages were read orignored, geographic information of the Internet Protocol (IP) address orother identifiers, device information such as computer make, model,type, and/or specifications, user demographic information, browsinghistory, web cookie data, and browser or other device and/or softwareidentifiers.

At step 210, the system may determine action and/or event level data,for example, at the traffic analysis server 125. For example, raw datamay be summarized by volume, frequency, type, etc. One or more splitsmay be determined, where each split defines a threshold for demarcatinghuman vs. non-human activity. For example, if one user sends threee-mails in one second, this frequency may exceed a threshold for humanactivity and enter a frequency range of non-human activity. Splits maybe determined for a summary feature and labeled for each action and/orevent based on which side of the threshold the variable falls for agiven user. The system may focus on a non-user-level summary feature.For example, if a variable pairing is username and IP address, thesystem may determine if a given username is spread in a humanlike ornon-humanlike manner across other IP addresses. For example, a human maylog in both from IP addresses that many users use (like a coffee shop IPaddress), as well as from an IP address that few users use (like aprivate residence). Bots, on the other hand, may spread evenly across IPaddresses, as a spammer may have purchased a given number of IPaddresses and load balance usage by bots. As a result, bots maydistribute differently on a curve of users per IP address. The splitdetermined for each pair may the form the second paired variable, or “B”side, for which counts may be later determined in step 225 discussedbelow.

At step 215, the raw data, which comprises data over a predeterminedtime range, may be divided into sub-time windows. Sub-time windows maycomprise, for example, weeks or months. Variable pairs may be determinedand/or generated, and may be associated with one or more sub-timewindows. Variable pairings may be considered candidate features, where afeature is a predictive or potentially predictive variable set. The datamay illustrate pair presence and frequency for each determinedthreshold. A threshold or split count and intra-split frequencythresholds may be determined. The system may determine if a given useror users behave consistently within time and/or sub-time windows, aswill be explained further below.

At step 220, pair frequency thresholds may be determined. For example,if the raw data has variables for successful logins and/or unsuccessfullogins, frequency thresholds may be determined for a number ofsuccessful logins that indicate that the user is likely a human, and/ora number of unsuccessful logins that indicate that the user is likelynon-human. Alternatively or in addition, the system may seek todetermine if the behavior of a given user is incidental or intentionaland consistent. It may be determined, for example, if a given user isreading a given sender's messages enough to meet a threshold ofconsistency. It may also be determined, for example, if the user islogging in often enough from a given IP address to meet a threshold ofconsistency. Steps 210, 215, and/or 220 may be optional.

At step 225, pair level data may be aggregated and/or furtheraggregated. Raw data may be collapsed to a pair level along with anyassociated counts. Action and/or event level data may be joined andlabels may be created for one or more pairs based on a determinedsplit/threshold. Pair frequency data and created labels for eachidentified split/threshold may be joined. Time sub-window data may bejoined and labels may be created based on any identified thresholds.Labels may indicate, for example, if any associated threshold has beenexceeded. An overall flag for each pair may be set. The flag mayindicate if the pair meets all or some predetermined number ofthresholds that the pair qualifies as human. If one or more pairs do notmeet one or more of the identified thresholds, the flag may also be setto indicate that the pair may not be human.

For example, many users check their messages at least once or twice perweek. From step 215, a 60-day data set might be broken up into 6 timesplits of 10 days each. A subsequent plot may be generated of usersmessage read and/or ignore behavior for each sender. Eachsender/recipient pair may be based on how the user's behavior lines upwith the rest of the population. The system may determine how many outof the 6 time splits a given user reads a given sender's messages (the‘split threshold’ or ‘split-level threshold’). The system may alsodetermine, within each 10-day split, on how many unique days does thegiven user read the given sender's mail (an ‘infra-split threshold’). Asimilar technique could be followed to determine consistence in the IPaddresses a person uses, based on authentication success/failure data.Users who exceed the derived thresholds in the combination of steps 215and 220 are the ones that may receive a “Q” for qualifying in step 225.

At step 230, user-level population data may be determined. User-levelcounts may be created that separate qualifying from non-qualifying pairsfrom the aggregated pair data. Thresholds of significance may bedetermined for each user-level count. Each threshold of significance maybe joined to the user-level data, for example, as a Boolean. Users forwhom all booleans in step 230 are true (which may mean that they exhibitstatistically significant interactions in all buckets formed by theproduct of the flags in step 210 with the qualifying/not qualifyingdetermination from step 225) may be a starting population of users thatthe feature that the system predicts to be human. This startingpopulation may be refined and expanded in subsequent steps, as will beexplained below.

In this manner, flags from step 210 may be combined with the qualifyingor not qualifying determinations from step 225. For example, for anembodiment regarding messaging systems, a flag from step 210 may be“other people read this sender's mail” (RBO) or not (NRBO), so the fourvariables would be Q-RBO, Q-NRBO, NQ-RBO, and NQ-NRBO given the twovariables. Human users might typically have instances in all fourvariables: the user reads the message and so do others, the user readsit but few others do, the user doesn't read it but other people do, andthe user doesn't read it and neither do others. But bots, for example,might not read any messages, regardless of whether others do, or atleast might not read messages consistently across multiple time windows.

At step 235, a list of known human users may be determined or imported.This list may be used in later steps to help determine if other usersare human. At step 240, distributions may be determined. For example,for each qualifying or not-qualifying pair count, side-by-sidedistribution plots may be graphed, and may split on whether a given useris human, such as with a true or false boolean. Alternatively, a graphneed not be generated, but rather the distribution of data may beanalyzed. As shown in FIGS. 3A-3D, the X-axis of each plot may be thequalifying or not qualifying count 305, and the Y-axis may be the numberof users 310. For human users, many will have low to medium counts ofeach qualifying/not qualifying component, and will follow a descendingcurve toward the higher counts (see FIG. 3A). Bots and/or abusive usersmay show little to no activity in the qualifying/non-qualifying plots,instead having a large spike at x=0. Non-qualifying activity counts canvary widely, but generally it will either fall more heavily on highercount values and cap out at much higher than human users (FIG. 3B), bemostly non-existent (FIG. 3C), or be highly concentrated (FIG. 3D).

At step 245, data may be cross-checked for overlap with other featuresthat predict humanness. For example, it may be noted if the user isassociated with a trusted IP address. Other features may include if theuser shows a humanlike pattern of logins. For example, it may beconsidered if a user logs in consistently from an IP address that isprobably a home, logs in from an IP address that is probably an office,logs in occasionally from diverse IP addresses like at a coffee shop,and/or logs in infrequently but consistently from another privateresidence. Although discussed in examples herein, steps, 235, 240, and245 may be optional steps. If the user always uses a small set ofdevices (device profiles), that may also further support the user beingconsidered human. If the user follows a consistent and/or sensiblegeo-profile, that may support the user being considered human. Whetherthe user has a believable address book may also be considered, and maybe considered both independently and in the context of other users.

Other features could also be joined as external sources. For example, itmay be considered by the system whether the user confirmed a non-VOIP orother mobile number via text message. The system may determine whetherthe user pays for features. The system may also determine ifconfirmation information, for example, a confirmation phone number, isused by more than a predetermined number of additional accounts (toomany may indicate non-human behavior). These external sources ofinformation may serve as a reference to fine tune the system to identifyhumans most accurately.

At step 250, it may be determined whether one or more of the users ishuman or non-human. Based on the form of the distributions in step 240,and any cross-checking with features/lists in steps 235 and 245, thelist of users tripping any or all Booleans may be considered realhumans. Although, some data scrubbing for outliers in intermediate datamay be occasionally used to achieve clean data distributions.

At step 255, based on the results of step 250, if there are users thatmay be close to qualifying as humans, but are not classified as such,the thresholds impacting qualification Q may be adjusted, and steps 225through 250 may be repeated. If any new users achieve a known humandistribution, those users may be re-classified as human. This step maybe iterative, and may be optional.

One example of an implementation related to electronic messaging willnow be discussed. Electronic messages may include e-mails, textmessages, social media messaging, etc. At step 205, raw data may bereceived with variables such as dates, electronic messaging recipient,electronic messaging sender, whether the message was read or ignored,and any other electronic message metadata and/or network metadata.

At step 210, action and/or event level data may be determined. Forexample, it may be determined what number or portion of users read oneor more sender's electronic messages. One or more threshold may bedetermined to identify senders whose electronic messages are read byothers (RBO). If variable pairings are email recipient and email sender,for example, a threshold may be determined to label the sender as“someone whose mail people tend to read.” The system may determine athreshold based upon the assumption that human users tend to read mailboth from senders that other users tend to read, and from some thatother users don't (private communications, etc. . . . ). The splitdetermined for each pair may the form the second paired variable forwhich counts may be later determined in step 225 discussed below.

At step 215, time sub-windows may be determined. For example, for agiven pair of variables, it may be determined in how many “n-day”windows did the pair appear out of the last “y” days. Determined “n-day”and “y-day” thresholds may capture consistent communication patterns.The windows determined may be any time deviation, including minutes,hours, weeks, etc.

At step 220, pair-level frequency data may be determined. For example,it may be determined how often each electronic message associated with agiven variable was read in the past predetermined time window, forexample 60 days. Independent and commingled thresholds may be determinedfor reads and/or ignores to capture engagement. Steps 210, 215, and/or220 may be optional.

At step 225, aggregated data may be determined. Aggregated/joined datasets may be generated, for example by pairing sets of variables from theraw data. Pairs that exceed a predetermined number of thresholds ofelectronic messages reads and ignores may be identified as qualifying(Q). A pair may be determined as qualifying, for example, if allassociated thresholds are met or exceeded. Additional factors for beingdetermined as qualifying (Q) may include a predetermined level ofreception of electronic messages from the user by others (others readthe messages), and a predetermined level of interaction with electronicmessages (the user reads electronic messages).

At step 230, user-level population data may be determined. One or moreuser counts may be created based upon data determined in previous steps.For example, variables may be created associated with userscorresponding to qualifying and electronic messages read by others(Q-RBO), qualifying and not read by others (Q-NRBO), not qualifying andread by others (N-RBO), and/or not qualifying and not read by others(N-NRBO). Significance thresholds may be determined for each variable,and each user may be labeled accordingly, for example with Booleans.Predicted human users may be determined as having ‘true’ for alllabels/variables. At step 235, the user-level counts may be checkedagainst a known human user list. This step may be optional. If theuser-level counts do not overlap with the known human user list morethan a predetermined level, the thresholds may be automatically reset toensure greater conformity.

At step 240, distributions may be determined. Human users maydemonstrate low to medium Q-RBO pairs, for example up to fifteen, lowbut extant Q-NRBO (1-3), and medium to high N-RBO and N-NRBO (10-30).Users not determined to be human may show no Q activity, and little tono N activity. This may be because bot accounts often tend to inviteusers to respond to a different single aggregated account rather thanthe source bot's address, since the bot system is probably usingthousands of accounts and couldn't monitor the messages the accountswould receive independently. Thus, bot accounts may tend to receive noelectronic messages, or very limited electronic messages from a fewsenders whose mailing lists they were signed up for on creation.Electronic messages that non-human accounts receive are rarely read, andif read, are rarely read consistently. There may be no Q activity due tothis lack of consistency, and since there is little or no messagesreceived at all, there may be little on the N side either. There mayinstead simply be a spike at 0 on the X-axis of the relevant plot(s).These factors may be incorporated into the determination as to whetherany given user account is human or non-human.

At step 245, as an option, other known predictors of humanness may beconsidered. For example, mail system features and a predetermined numberof believable logins associated with the users, for example over apredetermined time period, may be considered.

At step 250, based on the above-determined features, it may bedetermined whether a user is real/human. Based on the distributions,and/or cross-checks with existing features and lists of known users, anynewly determined features may be considered. Steps 210-250 may beiterated, with thresholds being tweaked with each iteration, in order tomore reliably determine whether each user is human or non-human, and/orto ensure greater conformity with any lists of known humans ornon-humans.

At step 255, further modifications and iterations may be performed. Forexample, some users communicate infrequently but the communications maystill be desired. To account for this situation, the formula for Q maybe modified to allow for 100% reads for a variable pair, if the sendingfrequency is low, but extant. This may be account for other individualssending direct (personal) emails.

Another example associated with FIG. 2 will now be discussed, focusingon login attempt analysis. At step 205 raw data may be received, whichmay include variables such as date, username, login IP address, and/orauthentication attempt results (e.g. succeed or fail).

At step 210, account and/or event level data may be determined. Forexample, a number of users that succeed in login authentication fromeach IP address may be determined. Thresholds may be determined forhigh-volume IP addresses versus more normal-volume IP addresses. Forexample, an IP address associated with a public library may have adifferent threshold from an IP address associated with a privateresidence. Thus, authentication success thresholds may be set based uponlogin volume, IP address location, overall traffic volume, etc.

At step 215, time sub-windows may be determined. For example, variablepairings may be evaluated to determine how often the pair appears in ‘N’unique days out of the last ‘Y’ months. N and Y thresholds may bedetermined to capture login consistency. At step 220, pair frequencydata may be determined. For example, it may be determined how many timeseach pair succeeded and failed in the last ‘X’ days. Authenticationfailure and success thresholds may be determined for active users.

At step 225, pair data may be aggregated and/or joined. Pairs thatexceed thresholds for login successes/failures in X days may bedetermined as qualifying (Q). Consistency thresholds for sufficientmonths out of the last Y months with at least N unique login days mayfurther be factored in determining if a pair is qualifying Q.

At step 230, user data may be determined. For example, user-level countsmay be created based upon received variables and/or pair data. Forexample, four counts may be generated based on qualifying high volume IPaddresses (Q-HV_IP), qualifying normal volume IP addresses (Q-NV_IP),non-qualifying high volume IP addresses (N-HV_IP), and non-qualifyingnormal volume IP addresses (N-NV_IP). Variables based upon additional orfewer categories of volume and levels of qualification may also begenerated. Significance threshold may be determined for each variable,and each user may be labeled accordingly, for example with Booleans.Predicted humans users may be determined as having ‘true’ for alllabels/variables

At step 235, a cross-check may be performed with known human users. Atstep 240, distribution plots may be generated. Predicted human userstypically show distributions starting with low counts for each category(e.g., 1-3 pairs), and tapering off by a dozen pairs. Those considerednon-human might show little to no activity on the Q plots. On the Nplots, non-human users may be tend to have more pairs with both highvolume and normal volume IP addresses (e.g. 10-20 pairs, or into thehundreds).

At step 245, the data may be cross-checked with known humannesspredictors, such as mail-send IP addresses (IP addresses from which agiven user consistently sends messages).

At step 250, a final determination may be made as to whether the usersare human or non-human. Based on the distributions determined in step240, and by possibly cross-checking with existing features and lists,one or more newly determined features may be accepted.

At step 255, steps described in this embodiment may be iterated, andthreshold levels adjusted, to help determine users that are near thehuman/non-human threshold, which may cause the predicted human list toexpand. Many devices have stored passwords, and would therefore have100% authentication success rate. The formula for Q may be modified toallow only successes (or only failures with the same password), insteadof requiring both, at higher threshold values of login frequency andconsistency. This may result in a larger number of predicted humanusers.

FIGS. 3A-3D depict graphs associated with exemplary methods foridentifying human users on a network, according to an exemplaryembodiment of the present disclosure. These figures may displaypair/feature counts on the X-axis 305, and number of users on the Y-axis310. FIG. 3A shows an example human activity distribution. FIG. 3B showsan example non-human distributed activity curve. FIG. 3C shows anexample non-human minimal activity curve, and FIG. 3D shows aconcentrated human activity curve.

These graphs may be generated in pairs, with one plotting thedistribution of the users being predicted as human in step 230, and theother a graph predicting non-human users. A set of these graphs may begenerated for each variable generated in step 230, and the human graphmay be compared to the non-human graph. For example, for the electronicmessaging embodiment discussed herein, two graphs may be generated eachfor Q-RBO, Q-NRBO, N-RBO, and N-NRBO. If the feature has appropriatethresholds set to predict human-ness, the human graphs may look similarto FIG. 3A, because humans typically act consistently at least “a littlebit” and inconsistently at least “a little bit.” This consistency orlack of consistency may apply to any or all variables, including log-inlocation, devices used, communicating with other users,succeeding/failing at login, or indicating that messages are spam. Incontrast, bots may typically lack diversity of behavior one way oranother. The same bot username might rarely do the same thing the sameway repeatably through time. Bots might claim to be logging in fromhighly diverse locations, or always the same location. Bots might alwaysclaim to be using the exact same version of the exact same browser onthe exact same device, or the claimed browser type, version number, anddevice might be different every time. Bots might send large volumes ofmessages to other users they've never interacted with before, or mightnever fail a password, or might never provide feedback as to whethermessages they receive are spam.

As a further example, the system might build a model to determine ifusers are likely human by assessing if the users have a believable arrayof devices. Human users might have a home PC with various browsers, awork PC with various browser, a tablet, smartphone, and occasionallyaccess public computers, etc. Raw data received may include a date,username, device, and action taken. Step 210 variables might be set tobe high/low activity devices based on daily actions taken on the daysthe device is used. Steps 215 and 220 might establish thresholds forconsistent usage of a given device over time. Variables for might bequalifying/not qualifying based on consistency, and high/low utilizationdevices. Human users might typically have devices that fall into allfour possibilities: “used often and you do a lot while using it” (e.g.,when on a smartphone), “used often, but fewer actions per use” (e.g.,when on a home PC), “not used often but you do a lot while using it”(e.g., on a tablet), and “not used often and you don't do much when youdo” (e.g., on an alternative browser). Two graphs for each of the fourvariable combinations may then be created at step 240. Humans may belikely to have activities on all graphs, while bots may show muchactivity on some graphs, and none on others. Bots may use dozens tohundreds of devices, and inconsistently over time, for example, as shownin FIG. 3B. Or, bots may always claim to be the same thing (e.g., aFirefox browser that disallows cookies, so no device profile exists).Even if a bot were to be labeled human, it would likely be caught whencross-referenced, for example, with a list of users who consistently login from the same IPs, as shown in steps 245 and 250. The system may betuned at step 255, for example, to relax certain thresholds to captureborderline cases. For example, if a possible human user doesn't loganything on “device not used often and few actions when used” chart,this might be forgiven, and the user may be labeled human nonetheless.

FIG. 4 depicts a flow diagram of an exemplary method for identifyinghuman users on a network, according to an exemplary embodiment of thepresent disclosure. At step 405, network data may be received comprisingdata transmitted over a network over predetermined time period, thenetwork data comprising a plurality of usernames and a plurality ofevents, wherein each of the plurality of events is associated with atleast one of the plurality of usernames. At step 410, a plurality ofpairs may be determined, each pair of the plurality of pairs comprisinga username of the plurality of usernames and an associated event of theplurality of events. At step 415, qualifying pairs of the plurality ofpairs may be determined, the qualifying pairs corresponding to a subsetof the plurality of pairs that meet or exceed one or more predeterminedevent frequency thresholds. At step 420, non-qualifying pairs of theplurality of pairs may be determined, the non-qualifying pairscorresponding to the subset of the plurality of pairs that do not meetor exceed one or more predetermined event frequency thresholds. At step425, at least one distribution plot may be generated associated with thequalifying pairs and non-qualifying pairs. At step 430, based on the atleast one distribution plot, it may be determined if at least oneusername of the plurality of usernames is associated with a human useror a non-human user.

The methods discussed in relation to FIGS. 2-4 substantially improve thetechnical field, by enabling better detection of human and non-humanusers. Embodiments herein describe a robust identification and scoringsystem to create and update records for determined human users.Human-indicative features may be extracted from network traffic onelectronic systems. Techniques described herein leverage humanlikequalifies such as having a home base, having habitual behaviors, lookinglike other users, acting like other users, interacting with other users,having relationships with other users, minimizing complexity and makingmistakes. Identified pairs of variables may form a hypothetical featurethat may predict human or non-humanness, and these features may bequantified or graphed. While the ‘embodiments’ component may not berequired, and any quantifiable features may be able to be tested for ahuman vs. non-human distinction, features tapping into predictivevariables tend to result in more accurate results. Once a feature iscreated, logical and statistical tests may be performed to determine ifit creates a valid and believable distribution. The steps may berepeated with other possible features to form a highly predictivefeature set. Hypothetical features may be cross-referenced to find anyoverlapping predictions. The strongest overlapping features may then befed into a learning algorithm that weighs and sums the contributingvectors in order to predict human or non-humanness for a given user witha high degree of accuracy.

FIG. 5 is a simplified functional block diagram of a computer that maybe configured as the network device 110 s, servers, CDN, platforms,and/or exchanges for executing the methods, according to exemplary anembodiment of the present disclosure. Specifically, in one embodiment,any of the network device 110 s, servers 120 or 125, CDN, platforms,and/or exchanges may be an assembly of hardware 500 including, forexample, a data communication interface 560 for packet datacommunication. The platform may also include a central processing unit(“CPU”) 520, in the form of one or more processors, for executingprogram instructions. The platform typically includes an internalcommunication bus 510, program storage, and data storage for variousdata files to be processed and/or communicated by the platform such asROM 530 and RAM 540, although the system 500 often receives programmingand data via network communications. The system 500 also may includeinput and output ports 550 to connect with input and output devices suchas keyboards, mice, touchscreens, monitors, displays, etc. Of course,the various system functions may be implemented in a distributed fashionon a number of similar platforms, to distribute the processing load.Alternatively, the systems may be implemented by appropriate programmingof one computer hardware platform.

Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine-readable medium. “Storage” type media include any or all of thetangible memory of the computers, processors or the like, or associatedmodules thereof, such as various semiconductor memories, tape drives,disk drives and the like, which may provide non-transitory storage atany time for the software programming. All or portions of the softwaremay at times be communicated through the Internet or various othertelecommunication networks. Such communications, for example, may enableloading of the software from one computer or processor into another, forexample, from a management server or host computer of the mobilecommunication network into the computer platform of a server and/or froma server to the mobile device. Thus, another type of media that may bearthe software elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links, or the like, also may be considered as mediabearing the software. As used herein, unless restricted tonon-transitory, tangible “storage” media, terms such as computer ormachine “readable medium” refer to any medium that participates inproviding instructions to a processor for execution.

While the presently disclosed methods, devices, and systems aredescribed with exemplary reference to transmitting data, it should beappreciated that the presently disclosed embodiments may be applicableto any environment, such as a desktop or laptop computer, an automobileentertainment system, a home entertainment system, etc. Also, thepresently disclosed embodiments may be applicable to any type ofInternet protocol.

Other embodiments of the disclosure will be apparent to those skilled inthe art from consideration of the specification and practice of thedisclosure disclosed herein. It is intended that the specification andexamples be considered as exemplary only, with a true scope and spiritof the disclosure being indicated by the following claims.

1-20. (canceled)
 21. A computer-implemented method for identifying human users on a network, comprising: receiving network data comprising data transmitted over a network over predetermined time period, the network data comprising a plurality of user-initiated events and a plurality of network addresses, wherein each of the plurality of network addresses is associated with at least one of the plurality of user-initiated events; determining a plurality of pairs, each pair of the plurality of pairs comprising a user-initiated event of the plurality of user-initiated events and an associated network address of the plurality of network addresses; determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more predetermined event frequency thresholds; determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more predetermined event frequency thresholds; generating at least one distribution associated with the qualifying pairs and non-qualifying pairs; and based on the at least one distribution, determining if at least one user-initiated event of the plurality of user-initiated events is associated with a human user or a non-human user.
 22. The method of claim 21, wherein the plurality of network addresses comprises a number of successful login attempts and/or a number of failed login attempts associated with each user-initiated event of the plurality of user-initiated events, and further comprising: determine if the at least one user-initiated event is associated with a human user or a non-human user based upon the number of successful login attempts and the number of failed login attempts for each of the plurality of user-initiated events.
 23. The method of claim 21, further comprising: determining at least one of the predetermined event frequency thresholds for each of the plurality of network addresses;
 24. The method of claim 21, further comprising: receiving additional network data associated with a plurality of known human users; and determining at least one of the predetermined event frequency thresholds based, at least in part, on the plurality of known human users.
 25. The method of claim 21, further comprising: associating, for the plurality of user-initiated events, a plurality of booleans with the one or more predetermined event frequency thresholds; determining, for each of the plurality of user-initiated events, whether at least one of the plurality of booleans is true or false based upon whether any of the one or more predetermined event frequency thresholds are met or exceeded; and determining if each of the plurality of user-initiated events is associated with a human user or a non-human user based upon whether each of the plurality of booleans is true or false and based upon the at least one distribution.
 26. The method of claim 25, further comprising: determining at least one uncertain user-initiated event of the plurality of user-initiated events that neither meets criteria for being associated with a human user or a non-human user; adjusting the one or more predetermined event frequency thresholds; and determining if the at least one uncertain user-initiated event is associated with a human user or a non-human user based upon the adjusted predetermined event frequency thresholds.
 27. The method of claim 21, further comprising: generating pairs of user-initiated events from the plurality of user-initiated events; determining communications between user-initiated events associated with each of the pairs of user-initiated events; and labeling the plurality of user-initiated events as trusted or untrusted based upon the communications between user-initiated events associated with each of the pairs of user-initiated events.
 28. A system for identifying human users on a network, the system including: a data storage device that stores instructions for identifying human users on a network; and a processor configured to execute the instructions to perform a method including: receiving network data comprising data transmitted over a network over predetermined time period, the network data comprising a plurality of user-initiated events and a plurality of network addresses, wherein each of the plurality of network addresses is associated with at least one of the plurality of user-initiated events; determining a plurality of pairs, each pair of the plurality of pairs comprising a user-initiated event of the plurality of user-initiated events and an associated network address of the plurality of network addresses; determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more predetermined event frequency thresholds; determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more predetermined event frequency thresholds; generating at least one distribution associated with the qualifying pairs and non-qualifying pairs; and based on the at least one distribution, determining if at least one user-initiated event of the plurality of user-initiated events is associated with a human user or a non-human user.
 29. The system of claim 28, wherein the plurality of network addresses comprises a number of successful login attempts and/or a number of failed login attempts associated with each user-initiated event of the plurality of user-initiated events, and the method further comprising: determine if the at least one user-initiated event is associated with a human user or a non-human user based upon the number of successful login attempts and the number of failed login attempts for each of the plurality of user-initiated events.
 30. The system of claim 28, the method further comprising: determining at least one of the predetermined event frequency thresholds for each of the plurality of network addresses;
 31. The system of claim 28, the method further comprising: receiving additional network data associated with a plurality of known human users; and determining at least one of the predetermined event frequency thresholds based, at least in part, on the plurality of known human users.
 32. The system of claim 28, the method further comprising: associating, for the plurality of user-initiated events, a plurality of booleans with the one or more predetermined event frequency thresholds; determining, for each of the plurality of user-initiated events, whether at least one of the plurality of booleans is true or false based upon whether any of the one or more predetermined event frequency thresholds are met or exceeded; and determining if each of the plurality of user-initiated events is associated with a human user or a non-human user based upon whether each of the plurality of booleans is true or false and based upon the at least one distribution.
 33. The system of claim 32, the method further comprising: determining at least one uncertain user-initiated event of the plurality of user-initiated events that neither meets criteria for being associated with a human user or a non-human user; adjusting the one or more predetermined event frequency thresholds; and determining if the at least one uncertain user-initiated event is associated with a human user or a non-human user based upon the adjusted predetermined event frequency thresholds.
 34. The system of claim 28, the method further comprising: generating pairs of user-initiated events from the plurality of user-initiated events; determining communications between user-initiated events associated with each of the pairs of user-initiated events; and labeling the plurality of user-initiated events as trusted or untrusted based upon the communications between user-initiated events associated with each of the pairs of user-initiated events.
 35. A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform a method for identifying human users on a network, the method including: receiving network data comprising data transmitted over a network over predetermined time period, the network data comprising a plurality of user-initiated events and a plurality of network addresses, wherein each of the plurality of network addresses is associated with at least one of the plurality of user-initiated events; determining a plurality of pairs, each pair of the plurality of pairs comprising a user-initiated event of the plurality of user-initiated events and an associated network address of the plurality of network addresses; determining qualifying pairs of the plurality of pairs, the qualifying pairs corresponding to a subset of the plurality of pairs that meet or exceed one or more predetermined event frequency thresholds; determining non-qualifying pairs of the plurality of pairs, the non-qualifying pairs corresponding to the subset of the plurality of pairs that do not meet or exceed one or more predetermined event frequency thresholds; generating at least one distribution associated with the qualifying pairs and non-qualifying pairs; and based on the at least one distribution, determining if at least one user-initiated event of the plurality of user-initiated events is associated with a human user or a non-human user.
 36. The computer-readable medium of claim 35, wherein the plurality of network addresses comprises a number of successful login attempts and/or a number of failed login attempts associated with each user-initiated event of the plurality of user-initiated events, and the method further comprising: determine if the at least one user-initiated event is associated with a human user or a non-human user based upon the number of successful login attempts and the number of failed login attempts for each of the plurality of user-initiated events.
 37. The computer-readable medium of claim 35, the method further comprising: receiving additional network data associated with a plurality of known human users; and determining at least one of the predetermined event frequency thresholds based, at least in part, on the plurality of known human users.
 38. The computer-readable medium of claim 35, the method further comprising: associating, for the plurality of user-initiated events, a plurality of booleans with the one or more predetermined event frequency thresholds; determining, for each of the plurality of user-initiated events, whether at least one of the plurality of booleans is true or false based upon whether any of the one or more predetermined event frequency thresholds are met or exceeded; and determining if each of the plurality of user-initiated events is associated with a human user or a non-human user based upon whether each of the plurality of booleans is true or false and based upon the at least one distribution.
 39. The computer-readable medium of claim 38, the method further comprising: determining at least one uncertain user-initiated event of the plurality of user-initiated events that neither meets criteria for being associated with a human user or a non-human user; adjusting the one or more predetermined event frequency thresholds; and determining if the at least one uncertain user-initiated event is associated with a human user or a non-human user based upon the adjusted predetermined event frequency thresholds.
 40. The computer-readable medium of claim 35, the method further comprising: generating pairs of user-initiated events from the plurality of user-initiated events; determining communications between user-initiated events associated with each of the pairs of user-initiated events; and labeling the plurality of user-initiated events as trusted or untrusted based upon the communications between user-initiated events associated with each of the pairs of user-initiated events. 